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Economically Optimum Reserve Margin (EORM) Workshop Economically Optimum Reserve Margin (EORM) Workshop

Economically Optimum Reserve Margin (EORM) Workshop - PowerPoint Presentation

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Economically Optimum Reserve Margin (EORM) Workshop - PPT Presentation

April 14 2017 ERCOT Astrapé Consulting LLC The Brattle Group 1 Introduction Workshop Agenda 3 Workshop Goals Provide stakeholders with a solid understanding of the EORMMERM modeling approaches ID: 815533

study reserve margin load reserve study load margin based year capacity peak energy 000 weather hours ordc market ercot

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Slide1

Economically Optimum Reserve Margin (EORM) WorkshopApril 14, 2017ERCOT, Astrapé Consulting LLC, The Brattle Group

Slide2

1. Introduction

Slide3

Workshop Agenda3

Slide4

Workshop GoalsProvide stakeholders with a solid understanding of the EORM/MERM modeling approaches

Consensus decision-making on methods for EORM/MERM studies

Stay

focused on methodological approaches and issues; we will not discuss opportunities for

supplementary

studies

4

Discuss each method used for 2014 Brattle/Astrapé study and NERC Loss of Load studies

Collaboratively modify the method

Identify unsatisfied concerns

Assess the degree of support

Finalize the method, or table for further discussion at follow-up conference call

Slide5

Summary of 2014 Optimal Reserve Margin StudyIn 2014 the PUCT asked Brattle and Astrapé to

estimate the economically-optimal reserve margin in ERCOT to inform their ongoing review of market design for resource adequacy (full study

linked here

)

Under base case assumptions, we

estimated

reserve margins*

of:

10.2% economic optimum

11.5% in energy-only equilibrium (also minimizes customer cost)

14.1% required to meet 1-in-10 Enforcing a 1-in-10 reserve margin requirement

at 14.1% (with or without a capacity market) would increase long-run average customer costs by approximately $400 million/year or 1% of retail rates relative to the current energy-only market in equilibrium (considering energy and capacity price impacts, not considering other costs and benefits)

A 14.1% reserve margin requirement would also reach equilibrium price levels sooner than an energy-only market, meaning near-term impacts would be greater

5

*2014 reported reserve margins used ELCC and other reserve margin accounting conventions at the time. Updated conventions would increase all reported numbers by 1.8%.

Slide6

Summary of 2014 Optimal Reserve Margin Study

We estimated a 14.1% reserve margin required to meet the traditional 1-in-10 loss of load event (LOLE) standard, results sensitive to:

Forward period

at which supply decisions are locked in, and consequential load forecast error (LFE) that needs to be considered in analysis (removing LFE drops the reserve margin to 12.6%)

Likelihood of 2011 weather

recurring, with this extreme year treated at 1% chance in base case (increasing to 1/15 or equal chance would increase the reserve margin to 16.1

%)

Estimated other reliability metrics as well

6

14.1% for 1-in-10

9.6% for 0.001% Normalized EUE

Reliability Metrics Across Reserve Margins

*

*2014 reported reserve margins used ELCC and other reserve margin accounting conventions at the time.

Slide7

2. Modeling the Electricity System

Slide8

SERVM Modeling Framework

Hourly chronological unit commitment and dispatch

Stochastic (Monte Carlo)

simulation involving thousands of model iterations

Simulate distributions for load/weather, load growth uncertainty, outages, fuel prices, intermittent renewable output, demand response, etc.

Simulate Emergency Operating Procedures

Simulate scarcity pricing during shortage events

Example based on recent Loss of Load studies conducted for ERCOT:

Weather

(13 years of weather history)

Economic load forecast error

(distribution of 5 points)Multi-state unit outage modeling, capturing frequency and duration (50 iterations)Probabilistic modeling of external assistance/DC ties, Private Use Network generating units

Total load scenario breakdown: 13 weather years x 5 LFE points = 65 scenarios; Total iteration breakdown: 65 scenarios * 50 unit outage iterations = 3,250 yearly simulations or until convergence is metModel run-time:

A single 50 iteration scenario takes ~ 30 minutes. These scenarios are simulated in parallel across multiple cores8

Slide9

Supply

Slide10

Thermal ResourcesModeled based on CDR and ERCOT internal production cost model data

Seasonal Capacity (Vary by hourly temperature)

Heat Rate Curves

VOM

Startup Costs

Fuel Prices

Startup Times

Emissions

Min-up/Min-down Times

Ramp Rates

Ancillary Service Capability AGC CapableQuick Start

10

Slide11

Forced/Planned Thermal Generator OutagesFull Outages

Time to Repair

Time to Failure

Partial Outages

Time to Repair

Time to Failure

Derate

Percentage

Startup Failures

Maintenance Outages

Planned Outages

Created based on Outage Scheduler data for December 2010 – July 201611

Unit Name

Capacity Weighted Equivalent Forced Outage Rate (%)

Nuclear

4.04%

Coal

6.32%

Gas Combined Cycle

4.27%

Gas Combustion Turbine

19.42%

Gas Steam Turbine

20.03%

Fleet

Capacity Weighted

Average

EFOR

8.50

%

*Values based on ERCOT’s latest Loss of Load study for NERC (

http://www.nerc.com/pa/RAPA/ra/Reliability%20Assessments%20DL/2016ProbA_Report_Final_March.pdf

)

Slide12

Forced/Planned Thermal Generator Outages12

Slide13

Private Use Network Resources13

Draw Probability

10%

10%

10%

10%

10%

10%

10%

10%

10%

10%

Load Level

Net Output (MW)

Above 100% of Peak Forecast

3,631

3,800

3,822

3,875

3,885

3,885

3,916

3,927

3,991

4,090

95% - 100% of Peak Forecast

3,042

3,222

3,403

3,583

3,764

3,809

4,026

4,243

4,459

4,676

90% - 95% of Peak Forecast

3,042

3,201

3,360

3,519

3,677

3,723

3,938

4,154

4,370

4,585

85% - 90% of Peak Forecast

2,633

2,860

3,087

3,314

3,541

3,587

3,825

4,063

4,302 4,540 70% - 85% of Peak Forecast 2,270 2,531 2,792 3,053 3,314 3,360 3,655 3,950 4,245 4,540 Below 70% of Peak Forecast 999 1,044 1,067 1,441 2,497 2,542 2,985 3,428 3,870 4,313

Net Output modeled stochastically

Most

Recent NERC Study: Output based on

load level as shown below

Previous EORM study: Output based on market price

Slide14

Hydro Resources13 years

of

historical hydro energy modeled

Peak

shaving capacity based

on regression above

14

Slide15

Hydro Resources15

Peak Shaving Capability

Maximum Output during August

Annual Energy

521 MW nameplate

Characterized resources based on:

4 years of hourly data from ERCOT

15 years of monthly data from FERC 923

Hydro resources modeled with different parameters each month:

Monthly total energy output

Daily peak shaving capability (from historical hourly data, recreated for other years based on

regression analysis)

Energy output is partly peak shaving (scheduled during daily load peaks) and partly run-of-river (output in off-peak hours)

Remaining hydro capacity (nameplate minus output) counts toward RRS and ORDC x-axis (approximately the same as 240 MW of

hydro-synchronous

resource that ERCOT assumes in PRC calculation)

Slide16

Switchable, Mothballed, Retired Units

Past studies based

on

CDR forecast/accounting rules

Switchable Units

:

Included as internal

resources with capacity deductions based on Notices of “Unavailable Capacity” submitted by SGR owners

Propose adopting a price response method similar to how PUN units are modeled

Retirements

:

Exclude starting in the CDR-specified year based on Notices of “Suspension of Operations of a Generation ResourceSeasonal Mothballs:

included 1,876 MW of seasonal mothballs that are available for dispatch only in summer months, nearly all of them from May-September Permanent Mothballs: Exclude

from modelPlanned New Units: Excluded from model to achieve low starting reserve margin in simulations; then increase with marginal unit

16

Slide17

Wind ModelingHourly shape by weather yearDeveloped by third party consultant by site

Aggregated to CDR values for study year

17

Slide18

Solar ModelingHourly shape by weather yearDeveloped by third party consultant by site

Aggregated to CDR values for study year

18

Slide19

Demand-Side ResourcesDispatched based on price subject to the following

Hours per season

Time of day

Weekday/Weekend

19

Slide20

Demand-Side Resources20

Summer Capacity

Call Limits

Call Priority

TDSP Standard Load Management Programs

208

16 hours per year, during hours 14-20

1

Load Resources Serving as Responsive Reserve

1,153

Unlimited

2

10 Min ERS

609

8 hours per season and per hourly intervals;

Seasons: Winter, Spring, Summer, Fall;

Hourly intervals: week day hours 1-8 and 21-24 and weekends, week day hours 9-13, week day hours 14-16, week day hours 17-20

3

30 Min ERS

898

8 hours per season and per hourly intervals;

Seasons: Winter, Spring, Summer, Fall;

Hourly intervals: week day hours 1-8 and 21-24 and weekends, week day hours 9-13, week day hours 14-16, week day hours 17-20

4

Slide21

Demand

Slide22

Weather Uncertainty on Loads22

Peak Load by Weather Year

(Before and After DR Gross-Up)

Load Duration Curves

(Peak Hours, Before DR Gross-Up)

Slide23

Economic Growth Uncertainty

Non-weather load forecast error (LFE) increases with forward period

Assume normally distributed forecast error with standard deviation of 0.8% on a 1-year forward basis, increasing by 0.6% with each additional forward year

Scale of error is a standard assumption developed in lieu of an ERCOT-specific analysis

We assume no bias or asymmetry in the non-weather LFE (unlike weather-driven LFE which has greater upside than downside uncertainty)

Modeling approach:

Assume resource decisions and reserve margin must be “locked in” 3 years forward, so realized forecast error is larger than if more shorter-term supply options were available

Sensitivity analysis examining impact of forward periods ranging from 0 to 4 years forward

23

Non-Weather Forecast Error

With Increasing Forward Period

3-Year Forward LFE

Discrete LFE Error Points Modeled

Slide24

Transmission

Slide25

Transmission Topology and Import/Export ModelingExternal Regions

Hourly Loads

Resources

DC

Ties (probabilistic for EORM study; based on historic flows during highest peak load hours for NERC Loss of Load study)

Share resources based on

economics

25

810 MW

280 MW

Mexico

(Coahuila, Nuevo Leon, & Tamaulipas)

ERCOT

5,180 MW

Entergy

(MISO)

SPP

Slide26

Generation Resource Mix

26

Values based on previous EORM study

Slide27

Diversity with External Regions27

Values based on previous EORM study

Slide28

The Market

Slide29

Representation of Energy and Ancillary Service MarketsEconomic reserve margin analyses need to simulate market dispatch and prices

Most important elements to model are those that characterize scarcity event:

System cost

(drives the optimal reserve margin)

Energy/ancillary prices

(drives the market equilibrium reserve margin

)

Recommend using a similar approach in future studies, but reviewing the need for significant market design updates

29

Unit Commitment

Week-ahead unit commitment

4-hour ahead unit commitment

Hourly dispatch of 10 & 30-min quickstart

Energy MarketHourly production cost model representing real-time energy market

Prices at marginal cost, considering generation and demand responseDetailed representation of scarcity event costs and pricing (see next slides)Scarcity prices affected by ancillary service shortage pricing

Ancillary Services

Regulation, spinning, and non-spinning reservesOperating Reserve Demand Curve (ORDC)

Power Balance Penalty Curve (PBPC)

Slide30

Scarcity Pricing and Emergency Procedures

30

Emergency Level

Marginal

Resource

Trigger

Price

Marginal

System Cost

n/a

Generation

Price

Approximately $20-$250

Same

n/a

Imports

Price

Approximately $20-$250

Up to $1,000

during load shed

Same

n/a

Non-Spin Shortage

Price

Marginal Energy +

Non-Spin ORDC

w/ X = 2,000

Marginal Energy +

Non-Spin ORDC w/

X = 1,150

n/a

Emergency Generation

Price

$500

Same

n/a

Price-Responsive Demand

Price

$250-$9000

Same

n/a

Spin Shortage

Price

Marginal Energy + Non-Spin

+

Spin ORDC

w/ X = 2,000

Marginal Energy + Non-Spin +

Spin ORDC w/

X = 1,150

n/a

Regulation Shortage

Price

Power Balance Penalty Curve

Same

EEA 1

30-Minute ERSSpin ORDC x-axis = 2,300 MW$3,239 at Summer Peak(from ORDC)$1,405EEA 1

TDSP Load Curtailments

Spin

ORDC x-axis

=

1,750 MW

$9,000 (from ORDC)

$2,450

EEA 2

Load

Resources in RRS

Spin

ORDC x-axis

=

1,700 MW

$9,000 (from ORDC)

$2,569

EEA 2

10-Minute

ERS

Spin

ORDC x-axis

=

1,300 MW

$9,000 (from ORDC)

$3,681

EEA 3

Load Shed

Spin

ORDC x-axis

=

1,150 MW

VOLL = $9,000

Same

Recommend reviewing 2014 study assumptions for material updates.

Slide31

Operating Reserve Demand Curve31

ORDC is one of the most important drivers of economic reserve margin

Important to distinguish the distinction between:

Marginal system cost

(blue curve) based on ERCOT’s analysis of the likelihood of lost load when running short of reserves

Price-setting ORDC

(red curve) at X = 2,000 used to set market prices

Recommend implementing updated ORDC curves (4 seasons, 6 times of day, 2 reserve types) and updating for design enhancements as needed

Operating Reserve Demand Curves

Example: Summer Hours 15-18

Sources and Notes:

Page 35, Figure 17, Brattle EORM

report.

Slide32

Selection of the Marginal Resource

Basic study approach is to add (or subtract) supply from the model until we find the cost-minimizing quantity (optimal reserve margin) or the maximum quantity that the market will attract (market equilibrium), see later slides

Outcome depends partly on what type of resource is added

If the market is near equilibrium among resource types, results will be similar regardless of this choice (e.g., 2014 study found similar results whether using gas CC or CT as the marginal resource)

Recommend future studies:

Use gas CC as the default type, given the greater quantity and uniformity of CC technologies

Maintain flexibility to examine CTs as a test, or examining other types if system conditions change

32

2014 Reserve Margin Study Results

Gas CC Marginal

Gas CT Marginal

Slide33

Cost of New Entry Estimate33

CONE

is

a significant driver of results, with higher CONE driving lower economic reserve margin

Subject to significant uncertainties, e.g., ATWACC

2014 study adapted PJM’s bottom-up engineering CONE estimate, applying adjustments for ATWACC, location, and interconnection costs

Recommend developing a forum for Market Participants to request CONE updates or sensitivity analyses; update process would consider formal CONE estimates from other markets and EIA, then use judgement to apply reasonable adjustments

Performance Characteristics

Sources and Notes:

Performance consistent with 2011 Brattle ERCOT Study after update of merchant ATWACC assumption.

One year of escalation adapted from 2013 ISO-NE ORTP parameter estimates.

2011 & 2014 Gross CONE Values

Slide34

Value of Lost Load (VOLL)Value of lost load (VOLL) is the cost imposed on customers when they face involuntary service interruptions

VOLL is subject to significant uncertainty depending on customer type and study approach. Estimates can range $1,000 to $100,000/MWh* (but high-end numbers not appropriate to use in this study since these high-value end uses should already have back-up power to protect against distribution outages)

In our 2014 study, we found that a VOLL range of $4,500 to $18,000/MWh translated to a 8.9%-11.8% range in the economically optimal reserve margin (that study also scaled DR curtailment costs in proportion to VOLL)

Recommend maintaining 2014 study approach that used the VOLL assumed in ERCOT nodal protocols and used as the High System-Wide Offer Cap, or $9,000/MWh

34

Notes

:

*Approximately $1,500

– $3,000/MWh for residential, $10,000 – $50,000/MWh for commercial, and $10,000 – $80,000/MWh for industrial loads according to a

MISO survey. See MISO “Value of Lost Load Final Report. May 15, 2006.

Slide35

3. Study Results

Slide36

Reserve Margin Accounting

CDR method:

Firm Load = Peak Load Forecast grossed up for Energy Efficiency

– Load Resources (ERS, providing Responsive Reserves)

– Energy Efficiency

Total Resources =

Thermal (Seasonal Net Sustained Capability)

+ Hydro peak capacity contribution

+

Wind/Solar seasonal peak average contribution

+ Switchable Capacity less amount unavailable

+ Available mothballed and RMR capacity

+ PUN capacity contribution forecast

+ DC Tie capacity contribution + Planned thermal resources + Planned wind/solar seasonal peak average contribution

 

36

Slide37

Reliability Based Reserve Margin TargetsNERC Assessment Metrics

LOLH

EUE

EUE / Net Energy for Load

Industry Standard

LOLE

1 day in 10 year standardEquivalent to 0.1 LOLE

37

Slide38

Reliability Based Reserve Margin Targets38

Reserve Margins Required to Meet Alternative Physical Reliability Standards

Base Case

Slide39

Reliability Based Reserve Margin TargetsEORM Study

39

Base Case

Assumptions:

1

% chance of 2011 weather

Load forecast error consistent with 3 years forward

CC as marginal technology

14.1%

reserve margin for 0.1 LOLE

Sensitivities

If 2011 is given equal weather weight (1/15 chance), the reserve margin increases to

16.1%

If non-weather LFE is excluded, the reserve margin drops to

12.6% LOLE w/ Differing 2011 Weather Weight

LOLE w/ Varying Forward Periods

Slide40

Renewable Effective Load Carrying Capability

ELCC Simulations: Not conducted for NERC Assessment or previous EORM

Average ELCC of wind and solar

Calibrate system to

0.1 LOLE

Remove entire wind/solar fleet and determine conventional capacity needed to maintain 0.1 LOLE

Average ELCC = conventional capacity added/ nameplate capacity of wind/solar fleet

Incremental ELCC of

wind (coastal or non coastal)

and

solar (fixed vs. tracking)Start at 0.1 LOLE

Add 1,000 MW of wind and remove conventional MW to maintain 0.1 LOLEELCC = Conventional MW removed / 1,000 MW of wind40

Slide41

Renewable Capacity Contributions

Use historical output rather than Effective Load Carrying Capability (ELCC) method

Current

CDR

capacity contribution methodology for

wind

resources

Develop a “Seasonal Peak Average” capacity factor (WINDPEAK%) by season and coastal/non-coastal regions

Based

on average historical availability (defined as a generator’s telemetered High Sustained Limit, or HSL) during the highest 20 seasonal peak load hours for an historical period of up to 10

yearsCapacity

contribution values are re-calculated after each season with new seasonal historical dataCoastal and non-coastal resources are calculated separately due to the significantly different diurnal wind patterns for these regionsMultiply WINDPEAK% by installed nameplate capacityFor

solar resources, similar approach; calculations done system-wide with only preceding three years of historical seasonal data

41

Slide42

4. STUDY PROCESS AND SCOPE

Slide43

Study Design Elements

EORM/MERM study updated every two years starting in mid-2018; aligns with current project schedule for conducting NERC’s biennial Probabilistic Assessments

Single simulation year, four years beyond study year (e.g., 2018 study would simulate the year 2022)

Continue to use CDR load and capacity variable definitions outlined in Protocols sections 3.2.6.2.1/ 3.2.6.2.2

Include all CDR planned resources with projected CODs as of December 31 of the year prior to the study year

Although not part of the EORM/MERM study effort, NERC requires a “Reference Margin Level” that serves as a resource adequacy performance benchmark

If ERCOT declines to provide NERC with a Reference Margin Level, NERC will use a 15% default value

43

Slide44

Economically Optimal Reserve Margin Results44

Economically Optimal

Reserve Margin at 10.2%

Total System Costs across Planning Reserve Margins

Notes:

Total

system costs include a large baseline of total system costs that do not change across reserve margins, including $15.2 B/year in transmission and distribution, $9.6 B/year in fixed costs for generators other than the marginal unit, and $10B/year in production costs.

Slide45

Market Equilibrium Reserve Margin Results45

Slide46

Purpose and Scope of Potential Sensitivity Analysis and ScenariosDefinitions

Sensitivity analysis:

U

sed to test robustness and uncertainty in a particular finding given uncertainties in individual parameters

Scenario analysis:

Used to test the performance of decisions (e.g. transmission plans) against fundamentally different futures

For the purposes of the 2018 EORM/MERM study, ERCOT and stakeholders should focus only on

sensitivity analysis

for key drivers

Develop a short prioritized list for consideration

46

Potential Sensitivities

(Drivers of EORM & MERM)

Forward period for load forecast uncertaintyWeighting of extreme weather years Gross Cost of New Entry

Marginal resource typeGas pricesValue of Lost Load (VOLL)

Quantity of intermittent resources

Slide47

Vetting of Study Results

ERCOT Proposal

Develop a study plan and project schedule to be presented to the Supply Analysis Working Group (SAWG)

Study plan should document any proposed deviations from the study methodology document and explain the rationale

Present the draft EORM/MERM study report to the SAWG, Wholesale Market Subcommittee, and PUCT staff for review and comment

Establish a new document section on the Resource Adequacy Webpage at ercot.com for making the final study report, associated public data, and report comments available for download

Codify, to the extent needed, this vetting process in the Nodal Protocols

47

Slide48

Periodic Methodology Assessments and Updates

48

ERCOT Proposal

During the study cycle off-year (2019, 2021, etc.), hold one or more SAWG meetings, with PUCT staff participation, by the end of Q3 to discuss the need for methodology updates

If any updates are warranted, ERCOT will modify the EORM/MERM methodology document by year end

ERCOT and SAWG meeting participants may agree on the need for sensitivity analysis prior to approving a change

Changes may include:

CDR-related Protocol revisions

Market-design-related Protocol revisions

Changes in key cost parameters such as CONE and VOLL

Source and derivation of model inputs (not the inputs themselves)

Updated methodology document posted to the Resource Adequacy Webpage at least two months prior to the start of the next study

Slide49

5. Next Steps

Slide50

Next Steps and Schedule

Agree on the need for a follow-up WebEx conference call (approximately mid-May or as

determined by

workshop participants)

Request for written follow-up comments: send them to Pete Warnken,

Pete.Warnken@ercot.com

Establish a sensitivity analysis work plan based on workshop participant requests, analysis scope/complexity, and

prioritization

; use the SERVM model and NERC 2016 LOL study data set

Provide PUCT staff and SAWG participants with periodic updates on EORM/MERM methodology document development progress

Complete and post the document by the end of 2017, contingent on the sensitivity work plan

50